Deep learning techniques, coupled with a growing accessibility of large-scale datasets, have largely improved the performance of many computer vision tasks including depth estimation [1]}. Depth estimation often uses either a monocular input or a stereo pair. For the monocular methods, a common practice is to train a single network to map RGB pixel to real-value depth [2]}, [3]}, [4]}, [5]}, mostly by learning from various monocular cues such as shape, lighting, shading, object type, etc. Stereo matching methods [6]}, [7]}, [8]}, on the other hand, learn the disparity by matching image patches from stereo pairs and later convert disparity to depth. Despite the significant improvement in monocular estimation methods, there is still a large gap between monocular and stereo depth accuracy
[9]}.
